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Record W2800139961 · doi:10.1213/ane.0000000000003337

Newborn Resuscitation Skills in Health Care Providers at a Zambian Tertiary Center, and Comparison to World Health Organization Standards

2018· article· en· W2800139961 on OpenAlexaff
Sara C. Mistry, Richard J. Lin, Hazel Mumphansha, Laura Kettley, Janaki A. Pearson, Sonia Akrimi, D Mayne, Wonder Hangoma, M. Dylan Bould

Bibliographic record

VenueAnesthesia & Analgesia · 2018
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineNeonatal resuscitationChecklistPsychological interventionAsphyxiaContext (archaeology)ResuscitationAnesthesiologyObservational studyEmergency medicineHealth careFamily medicinePediatricsNursingAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Birth asphyxia is a leading cause of early neonatal death. In 2013, 32% of neonatal deaths in Zambia were attributable to birth asphyxia and trauma. Basic, timely interventions are key to improving outcomes. However, data from the World Health Organization suggest that resuscitation is often not initiated, or is conducted suboptimally. Currently, there are little data on the quality of newborn resuscitation in the context of a tertiary center in a lower-middle income country. We aimed to measure the competencies of clinical practitioners responsible for newborn resuscitation. METHODS: This observational study was conducted over 5 months in Zambia. Health care professionals were recruited from anesthesia, pediatrics, and midwifery. Newborn skills and knowledge were examined using the following: (1) multiple-choice questions; (2) a ventilation skills test; and (3) 2 low-medium fidelity simulation scenarios. Participant demographics including previous resuscitation training and a self-efficacy rating score were noted. The primary outcome examined performance scores in a simulated scenario, which assessed the care of a newborn that failed to respond to basic interventions. Secondary outcome measures included apnea times after delivery and performance in the other assessments. RESULTS: Seventy-eight participants were enrolled into the study (13 physician anesthesiology residents, 13 pediatric residents, and 52 midwives). A significant difference in interprofessional performance was observed when examining checklist scores for the unresponsive newborn simulated scenario (P = .006). The median (quartiles) checklist score (out of 18) was 14.0 (13.0-14.75) for the anesthesiologists, 11.0 (8.5-12.3) for the pediatricians, and 10.8 (8.3-13.9) for the midwives. A score of 14 or more was required to pass the scenario. There was no significant difference in performance between participants with and without previous newborn resuscitation training (P = .246). The median (quartiles) apnea time after delivery was significantly different between all groups (P = .01) with anesthetic and pediatric residents performing similarly, 61 (37-97) and 63 (42.5-97.5) seconds, respectively. The midwifery participants displayed a significantly longer apnea time, 93.5 (66.3-129) seconds. Self-efficacy rating scores displayed no correlation between confidence level and the primary outcome, Spearman coefficient 0.06 (P = .55). CONCLUSIONS: Newborn resuscitation skills among health care professionals are varied. Midwives lead the majority of deliveries with anesthesiologists and pediatricians only being present at operative or high-risk births. It is therefore common that midwifery practitioners will initiate resuscitation. Despite this, midwives perform poorly when compared to anesthesia and pediatric residents. To address this discrepancy, a multidisciplinary, simulation-based newborn resuscitation program should be considered with continual clinical reenforcement of best practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.362
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations12
Published2018
Admission routes1
Has abstractyes

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